1. Field of the Invention
The disclosure relates to an image processing system, and in particular relates to a moving object detection technique for a video sequence.
2. Description of the Related Art
Moving object detection is an important technique for developing an intelligent video surveillance system in many applications. For example, in the security monitoring domain, it is not necessary for a security person to fix his eyes on a monitor always since the surveillance system will produce an alarm signal to him/her when a moving object is detected in a monitored region. In the applications in the medical industry, a remote medical care system with the ability of moving object detection will notify the medical care personnel to take care of the patient when some unusual behaviors, such as falling on the floor or rising from the bed, are detected by the system.
In general, the technique of moving object detection can be achieved through checking a distance map that is consisted of pixel-wise intensity distances between a received image and a reference image. The reference image can be either a background scene or a last adjacent image, which is designed according to the purpose of the system. For example, the moving object detection using the last adjacent image as the reference is generally used for object motion analysis. The threshold value for the distance checking affects the accuracy of moving object detection directly and has to be adaptively changed for different illumination conditions and cameras. The threshold value can be set manually by skilled person when deploying the system. However, the cost of manually setting is high for large scale deployment and the threshold value is fixed after the setting process so that the system can not adapt to the illumination change in some applications.
To reduce the cost of manually setting and to improve the robustness, P. L. Rosin, etc. (Pattern Recognition Letters 24, 2003, pp. 2345-2356) apply a global thresholding technique that is originally developed for image segmentation to obtain a threshold value for moving object detection. Many thresholding techniques that are proposed based on different assumptions of intensity distributions of pixels in an image have been evaluated experimentally. As shown in the experiments, the accuracies of the moving object detection using the thresholding technique of the image segmentation not only fluctuate significantly, but also can become lower than those of the manually setting even under steady illumination condition. The results can be explained by the assumption mismatch that is caused by the significant difference between the intensity distributions and the distance distributions of pixels in an image.
A system providing an adaptive thresholding for moving object detection but without suffering from accuracy degradation is desired.